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2. File:Discrete probability distribution with a countable set ...

   en.wikipedia.org/wiki/File:Discrete_probability...

   Diagram based on File:Discrete probability distribution.svg Polski: Rozkład o wartości 1/n dla argumentu w przedziale [1/(n+1); 1/n) dla każdej naturalnej wartości n, zero dla argumentu poniżej 0 oraz jeden dla argumentu 1 lub powyżej.

3. File:Discrete probability distribution illustration.svg

   en.wikipedia.org/wiki/File:Discrete_probability...

   English: From top to bottom, the cumulative distribution function of a discrete probability distribution, continuous probability distribution, and a distribution which has both a continuous part and a discrete part. Cumulative distribution functions are examples of càdlàg functions.

4. Poisson distribution - Wikipedia

   en.wikipedia.org/wiki/Poisson_distribution

   In probability theory and statistics, the Poisson distribution (/ ˈ p w ɑː s ɒ n /; French pronunciation:) is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time if these events occur with a known constant mean rate and independently of the time since the last event. [1]

5. Probability distribution - Wikipedia

   en.wikipedia.org/wiki/Probability_distribution

   A discrete probability distribution is applicable to the scenarios where the set of possible outcomes is discrete (e.g. a coin toss, a roll of a die) and the probabilities are encoded by a discrete list of the probabilities of the outcomes; in this case the discrete probability distribution is known as probability mass function.

6. Hypergeometric distribution - Wikipedia

   en.wikipedia.org/wiki/Hypergeometric_distribution

   In probability theory and statistics, the hypergeometric distribution is a discrete probability distribution that describes the probability of successes (random draws for which the object drawn has a specified feature) in draws, without replacement, from a finite population of size that contains exactly objects with that feature, wherein each draw is either a success or a failure.

7. Balls into bins problem - Wikipedia

   en.wikipedia.org/wiki/Balls_into_bins_problem

   The efficiency of accessing a key depends on the length of its list. If we use a single hash function which selects locations with uniform probability, with high probability the longest chain has (⁡ ⁡ ⁡) keys. A possible improvement is to use two hash functions, and put each new key in the shorter of the two lists.